from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	Dict         =    logging.get_logger(__name__)

__a    :	Tuple         =    {
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					int     					=		'''lilt'''





			def __init__(  self ,  lowerCAmelCase__=3_05_22 ,  lowerCAmelCase__=7_68 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=30_72 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=5_12 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=1E-12 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__="absolute" ,  lowerCAmelCase__=None ,  lowerCAmelCase__=4 ,  lowerCAmelCase__=10_24 ,  **lowerCAmelCase__ ,  )	->   Tuple:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__ ,  **lowerCAmelCase__		)

							__lowercase					     =				vocab_size
							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				hidden_act
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob
							__lowercase					     =				max_position_embeddings
							__lowercase					     =				type_vocab_size
							__lowercase					     =				initializer_range
							__lowercase					     =				layer_norm_eps
							__lowercase					     =				position_embedding_type
							__lowercase					     =				classifier_dropout
							__lowercase					     =				channel_shrink_ratio
							__lowercase					     =				max_ad_position_embeddings
import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Union[str, Any]         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				if "model" in sd.keys():
								__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)['''model''']

				# pop unnecessary weights
				__lowercase					     =				[
				    '''decoder.version''',
				    '''decoder.output_projection.weight''',
				]
				for key in keys_to_delete:
								if key in sd:
												sd.pop(lowercase				)

				__lowercase					     =				{
				    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
				    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
				    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
				    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
				}
				for old_key, new_key in keys_to_rename.items():
								if old_key in sd:
												__lowercase					     =				sd.pop(lowercase				)

				__lowercase					     =				list(sd.keys()				)
				for key in keys:
								if ".qkv_proj." in key:
												__lowercase					     =				sd[key]
												# We split QKV in separate Q,K,V

												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.q_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.k_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.v_proj.'''				)

												__lowercase					     =				value.shape[0]
												assert depth % 3 == 0
												# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
												# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
												__lowercase     ,	__lowercase     ,	__lowercase					     =				torch.split(lowercase				,     depth // 3				,     dim=0				)

												__lowercase					     =				q
												__lowercase					     =				k
												__lowercase					     =				v
												del sd[key]

				return sd





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=None				):







				"""simple docstring"""



				__lowercase					     =				load_checkpoint(lowercase				)

				if config is not None:
								__lowercase					     =				OPTConfig.from_pretrained(lowercase				)
				else:
								__lowercase					     =				OPTConfig()

				__lowercase					     =				OPTModel(lowercase				).half().eval()
				model.load_state_dict(lowercase				)

				# Check results
				Path(lowercase				).mkdir(exist_ok=lowercase				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--fairseq_path""",
		    type=str,
		    help=(
		        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
		        """ https://huggingface.co/models?other=opt_metasq"""
		    ),
		)
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
		__a    :	Any         =    parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)